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In recent years, sensors from smart consumer devices have shown greatdiagnostic potential in movement disorders. In this context, data modalitiessuch as electronic questionnaires, hand movement and voice captures havesuccessfully captured biomarkers and allowed discrimination between Parkinson'sdisease (PD) and healthy controls (HC) or differential diagnosis (DD). However,to the best of our knowledge, a comprehensive evaluation of assessments with amulti-modal smart device system has still been lacking. In a prospective studyexploring PD, we used smartwatches and smartphones to collect multi-modal datafrom 504 participants, including PD patients, DD and HC. This study aims toassess the effect of multi-modal vs. single-modal data on PD vs. HC and PD vs.DD classification, as well as on PD group clustering for subgroupidentification. We were able to show that by combining various modalities,classification accuracy improved and further PD clusters were discovered.

Subgroup discovery of Parkinson's Disease by utilizing a multi-modal smart device system